ObjectivesTo systematically review the efficacy of kinesio taping on post stroke shoulder pain.MethodsPubMed, EMbase, The Cochrane Library, Web of Science, PEDro, CNKI and WanFang Data databases were electronically searched to collect randomized controlled trials (RCTs) of kinesio taping on shoulder pain after stroke from inception to November, 2018. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies, then, meta-analysis was performed by using RevMan 5.3 software.ResultsA total of 8 RCTs involving 525 patients were included. The results of meta-analysis showed that, compared with the control group, kinesio taping group for 4 weeks treatment significantly reduced shoulder pain (SMD=−0.81, 95%CI −0.58 to −0.04, P=0.04), increased range of motion of shoulder flexion (SMD=0.59, 95%CI 0.17 to 1.01, P=0.006) and abduction (SMD=0.67, 95%CI 0.24 to 1.09, P=0.002). It also improved Fugl-Meyer upper limb function (SMD=1.00, 95%CI 0.25 to 1.76, P=0.009).ConclusionsCurrent evidence shows that the kinesio taping for 4 weeks duration can effectively reduce shoulder pain after stroke. Due to limited quality and quantity of the included studies, more high-quality studies are required to verify above conclusions.
Objective This article takes the construction of the Huaxi Hongyi Medical University model as the core, explores its application effect in assisting the generation of medical records, and provides a practical path for the construction and application of artificial intelligence in medical institutions. Methods Through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, a large-scale medical model with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, the average completion time of writing was reduced from 21 minutes to 5 minutes (a decrease of 76.2%), the accuracy rate of the model output reached 92.4%, and the annotation consistency Kappa coefficient was 0.85. Conclusion The model of medical institutions constructing independently controllable large-scale medical models and incubating various applications based on them can provide a reference path for the artificial intelligence construction of similar institutions.